Selecting prospective bidders to whom to promote an online auction based upon bidding history

ABSTRACT

A facility for identifying users to whom to promote a selected auction is described. The facility maintains a representation of bidding histories of a plurality of users. At a point at which certain users have already bid in the selected auction, the facility identifies users that have not already bid in the selected auction that have bidding histories that are similar to those of users that have already bid in the selected auction. The facility then promotes the selected auction to the identified users.

CROSS REFERENCE TO RELATED APPLICATION

[0001] This application claims the benefit of U.S. Provisional PatentApplication No. 60/171,843 filed Dec. 22, 1999 which is herebyincorporated by reference.

TECHNICAL FIELD

[0002] The present invention is directed to the field of electroniccommerce, and, more particularly, to the field of online marketingtechniques.

BACKGROUND

[0003] The auction is a sales technique in which prospective buyers arepermitted to bid on an item offered for sale, and the item is sold tothe bidder submitting the highest bid.

[0004] Online auctions have recently emerged, in which auctions areconducted on a World Wide Web site or in a similar environment. Users ofcomputer systems connected to the computer system on which the onlineauction is conducted can view descriptions and pictures of items offeredfor sale, as well as bid on items using their computer systems.

[0005] Operators of online auctions derive revenue from the onlineauctions in a variety of ways. For example, an online auction operatormay charge sellers a flat sales fee to auction each item, or may chargesellers a sales fee measured by a percentage of the sale price.Alternatively, an online auction operator may sell advertising that isviewed by sellers and/or bidders.

[0006] Generally, however, the level of profitability of online auctionsto their operator is directly related to the average number of biddersthat bid in each auction. Where an auction operator charges sellers asales fee measured by a percentage of the sale price, a larger number ofbidders produces a higher sales price, from which the operator collectsa higher sales fee. Where an auction operator charges sellers a salesfee of any kind, a larger number of bidders produces a higher salesprice, and a larger seller profit, thereby encouraging sellers toinitiate more auctions that produce more sales fees for the operator.Where an auction operator sells advertising on the auction site, alarger number of bidders produces a larger number of advertisingimpressions, and therefore greater advertising revenue.

[0007] Accordingly, an effective technique for increasing the number ofbidders bidding in an online auction would have significant utility.

BRIEF DESCRIPTION OF THE DRAWINGS

[0008]FIG. 1 is a high-level block diagram of the computer system uponwhich the facility preferably executes.

[0009]FIG. 2 is a flow diagram showing the steps preferably performed bythe facility in order to identify users to whom to promote a selectedonline auction once the auction has begun based upon the identities ofthe users that have already bid in other auctions.

[0010]FIG. 3 is a flow diagram showing the steps preferably performed bythe facility in order to identify users to whom to promote a selectedonline auction once the auction has begun using clusters of users eachhaving similar bidding histories.

[0011]FIG. 4 is a display diagram showing the manner in which thefacility preferably promotes an auction to identified users.

DETAILED DESCRIPTION

[0012] A software facility for selecting prospective bidders to whom topromote an online auction based upon bidding history (“the facility”) isprovided. The facility operates in conjunction with an online auctionsystem that tracks the users bidding on each of a number of onlineauctions.

[0013] For an auction selected for promotion, the facility obtains alist of the bidders that have bid in the selected auction. The facilityuses this list to identify other auctions, either completed or inprogress, in which at least a threshold percentage of the bidders in theselected auction have also bid. In each identified auction, the facilityidentifies the bidders that have not yet bid on the selected auction.The facility may also use clustering techniques to identify users havingbidding histories that are similar to those of the bidders in theselected auction.

[0014] The facility then promotes the selected auction to the identifiedbidders. In some embodiments, the facility sends an email message to theidentified bidders suggesting that they bid in the selected auction. Inother embodiments, the facility displays a suggestion to bid in theselected auction on a web page served to the identified bidders.

[0015] In this manner, the facility promotes the selected auction to agroup of bidders that both (1) is likely to be receptive to receivinginformation about the selected auction, and (2) is likely to actuallybid in the selected auction, thereby increasing the number of bidders inthe selected auction and increasing profitability for the auctionoperator.

[0016] In additional embodiments, the facility is applied to marketsother than online auctions, such as web merchants that sell items for afixed price. Where a large percentage of the purchasers of a first itemalso purchase a second item, the facility preferably promotes the firstitem to purchasers of the second item that have not purchased the firstitem, and vice versa.

[0017] Further embodiments of the facility are applied across markets.For example, where a large percentage of the users that purchased aparticular item from a web merchant also bid in a particular auction,the facility preferably promotes the auction to the purchasers that havenot yet bid, and the purchase to the bidders that have not yetpurchased.

[0018]FIG. 1 is a high-level block diagram of the computer system uponwhich the facility preferably executes. The computer system 100 containsone or more central processing units (CPUs) 110, input/output devices120, and a computer memory (memory) 130. Among the input/output devicesis a persistent storage device 121, such as a hard disk drive, and acomputer-readable media drive 122, which can be used to install softwareproducts, including components of the facility, which are provided on acomputer-readable medium, such as a CD-ROM. The input/output devicesalso include a network connection 123, through which the computer system100 may by connected to the network to be analyzed by the facility. Thememory 130 preferably contains the prospective bidder identificationfacility 131, as well as a bidding history representation 132 and apromotion history representation 133 both preferably generated and usedby the facility. While items 131-133 are preferably stored in memorywhile being used, those skilled in the art will appreciate that theseitems, or portions of them, may be transferred between memory and thepersistent storage device for purposes of memory management and dataintegrity. While the facility is preferably implemented on a computersystem configured as described above, those skilled in the art willrecognize that it may also be implemented on computer systems havingdifferent configurations, or distributed across multiple computersystems.

[0019] To more fully illustrate its implementation and operation, thefacility is described in conjunction with an example.

[0020]FIG. 2 is a flow diagram showing the steps preferably performed bythe facility in order to identify users to whom to promote a selectedonline auction once the auction has begun based upon the identities ofthe users that have already bid in other auctions. In steps 201-209, thefacility loops through each of a set of auctions other than the selectedauction in which users have bid. In a preferred embodiment, the set ofauctions includes both incomplete and completed auctions. In step 202,if more than a threshold number of users that bid in the selectedauction bid in the current auction, then the facility continues in step203, else the facility continues in step 209. The threshold used by thefacility may be either an absolute threshold expressed in number ofusers, or a relative threshold, expressed as a percentage of the usersbidding in the selected auction. In one embodiment, the facility usesthe relative threshold of seventy percent of the users bidding in theselected auction.

[0021] In steps 203-208, the facility loops through each user that bidin the current auction. In step 204, if the user has also already bid inthe selected auction, then the facility continues in step 208 to skipthe user, else the facility continues in step 205. In step 205, if theselected auction has already been promoted to the user, then thefacility continues in step 208 to skip the user, else the facilitycontinues in step 206. In step 206, the facility promotes the auction tothe user, since the user bid in a related auction, but has not yet bidin the selected auction or had the selected auction promoted to him orher. The process of promoting the selected auction to a user isdiscussed in greater detail below in conjunction with FIG. 4. In step207, the facility adds the user to the list of users to whom the auctionhas been promoted. In step 208, if additional users remain, then thefacility loops back to step 203 to process the next user. After the loopof steps 203-208 has completed, the facility continues in step 209. Instep 209, if additional auctions in the set remain, then the facilityloops back to step 201 to process the next auction in the set. After theloop of steps 201-209 is completed, these steps conclude.

[0022] Tables 1 and 2 below show the result of applying the steps shownin FIG. 2 to sample auctions. Table 1 shows the state in which thefacility identifies users to whom to promote an auction called auction3. TABLE 1 auction 1 auction 2 auction 3 auction 3 already biddersbidders bidders promoted to user 1 user 2 user 7 user 5  user 2 user 5user 11 user 22 user 3 user 7 user 12 user 4 user 11 user 13 user 5 user12 user 6 user 19 user 7 user 22

[0023] Table 1 shows a list of the users that have bid in auction 1, alist of the users that have bid in auction 2, a list of the users thathave bid in auction 3, and a list of the users to whom auction 3 hasalready been promoted. In order to identify users to whom to promoteauction 3, the facility examines the lists of users that have bid inauctions 1 and 2. In the example, the facility applies the thresholdthat, in order for another auction to be considered, at least seventypercent of the users that bid in the selected auction must have bid inthe other auction. In terms of the number of users that have bid inauction 3 in the example, this means that at least three of the usersthat have bid in auction 3 must have bid in each of the other twoauctions for them to be considered (4×70%=2.8).

[0024] It can be seen that only one of the users that bid in auction 3(user 7) also bid in auction 1. Since 1 is less than 3, the facilitydoes not use the list of users that have bid in auction 1. On the otherhand, it can be seen that three of the users that bid in auction 3 (user7, user 11, and user 12) also bid in auction 2. Because 3 is at least aslarge as 3, the list of users that have bid in auction 2 is considered.The facility considers the list of users that have bid in auction 2 asfollows: user 2 has not bid in auction 3, and auction 3 has not yet beenpromoted to user 2, so the facility promotes auction 3 to user 2;auction 3 has already been promoted to user 5, so the facility does notpromote auction 3 to user 5; user 7 has already bid in auction 3, so thefacility does not promote auction 3 to user 7; user 11 has already bidin auction 3, so the facility does not promote auction 3 to user 11;user 12 has already bid in auction 3, so the facility does not promoteauction 3 to user 12; user 19 has not bid in auction 3, and auction 3has not yet been promoted to user 19, so the facility promotes auction 3to user 19; and auction 3 has already been promoted to user 22, so thefacility does not promote auction 3 to user 22. Accordingly, in applyingthe steps shown in FIG. 2, the facility determines to promote auction 3to the users shown in Table 2 below. TABLE 2 promote auction 3 to user 2user 19

[0025] In some embodiments, the facility preferably uses clusters ofusers that are generated in such a way that the users in each clusterhave similar bidding histories. Such clusters are preferably generatedusing well-known clustering techniques, such as those described in AnilK. Jain and Richard C. Dubes, Algorithms for Clustering Data, 1988, pp.629-799; Phipps Arabie et al., Clustering and Classification, 1996;Richard A. Johnson and Dean A. Wichem, Applied Multivariate StatisticalAnalysis, 1998; and/or Leonard Kaufman and Peter J. Rousseau, FindingGroups in Data: An Introduction to Cluster Analysis, 1990.

[0026]FIG. 3 is a flow diagram showing the steps preferably performed bythe facility in order to identify users to whom to promote a selectedonline auction once the auction has begun using clusters of users eachhaving similar bidding histories. The facility preferably performs thesesteps once for each bid that is received in the selected auction. Insome embodiments, these steps are only performed in response to a user'sfirst bid in the selected auction. The steps may be performed directlyin response to each bid, or may be deferred for later performance in abatch mode. In step 301, the facility receives a bid in the selectedauction from a selected user. In steps 302-310, the facility loopsthrough each of a set of user clusters to which the selected userbelongs. In step 303, if more than a threshold number of users in thecurrent cluster bid in the selected auction, then the facility continuesin step 304, else the facility continues in step 310. The threshold usedby the facility may be either an absolute threshold expressed in numberof users, or a relative threshold, expressed as a percentage of users inthe current cluster. In one embodiment, the facility uses the relativethreshold of fifty percent of the users in the current cluster. In steps304-309, the facility loops through each user in the current cluster. Instep 305, if the current user bid in the selected auction, then thefacility continues in step 309 to skip the user, else the facilitycontinues in step 306. In step 306, if the selected auction has alreadybeen promoted to the current user, then the facility continues in step309 to skip the current user, else the facility continues in step 307.In step 307, the facility promotes the auction to the current user,since the current user is a member of the cluster, but has not yet bidin the selected auction or had the selected auction promoted to him orher. The process of promoting the selected auction to a user isdiscussed in greater detail below in conjunction with FIG. 4. In step308, the facility adds the user to the list of users to whom the auctionhas been promoted. In step 309, if additional users remain in thecluster, then the facility loops back to step 303 to process the nextuser. After the loop of steps 304-309 has completed, the facilitycontinues in step 310. In step 310, if additional clusters remain, thenthe facility loops back to step 302 to process the next cluster. Afterthe loop of steps 302-310 is completed, these steps conclude.

[0027] Tables 3 and 4 below show the result of applying the steps shownin FIG. 3 to sample auctions and clusters. Table 3 shows the state inwhich the facility identifies users to whom to promote auction 3. TABLE3 cluster 1 cluster 2 auction 3 auction 3 already members membersbidders promoted to user 7 user 7 user 7 user 5  user 8 user 8 user 11user 22 user 24 user 11 user 12 user 27 user 12 user 13 user 99 user 13user 101 user 19 user 22

[0028] Table 3 shows a list of the users in cluster 1, a list of theusers in cluster 2, a list of the users that have been in auction 3, anda list of the users to whom auction 3 has already been promoted. Inorder to identify users to whom to promote auction 3, the facilityexamines the lists of users that are members of clusters 1 and 2. In theexample, the facility applies the threshold that, in order for a clusterto be considered, at least fifty percent of the users in the clustermust have bid in the selected auction. It can be seen that only one ofthe six in cluster 1 (user 7) is among the four users that have bid inauction 3. Because seventeen percent (1 user÷6 users 17%) is less thanfifty percent, the facility does not use the list of users in cluster 1.On the other hand, it can been seem that four of the seven members ofcluster 2 (user 7, user 11, user 12, and user 13) have bid in auction 3.Because fifty-seven percent (4 user÷7 users =57%) is greater than fiftypercent, the list of users in cluster 2 is considered. The facilityconsiders the list of users in cluster 2 as follows: user 7 has alreadybid in auction 3, so the facility does not promote auction 3 to user 7.User 8 has not bid in auction 3, and auction 3 has not yet been promotedto user 8, so the facility promotes auction 3 to user 8; user 11 hasalready bid in auction 3, so the facility does not promote auction 3 touser 11; user 12 has already bid in auction 3, so the facility does notpromote auction 3 to user 12; user 13 has already bid in auction 3, sothe facility does not promote auction 3 to user 13; user 19 has not bidin auction 3, and auction 3 has not yet been promoted to user 19, so thefacility promotes auction 3 to user 19; and auction 3 has already beenpromoted to user 22, so the facility does not promote auction 3 to user22. Accordingly, in applying the steps shown in FIG. 3, the facilitydetermines to promote auction 3 to the user shown in Table 4 below.TABLE 4 promote auction 3 to user 8 user 19

[0029]FIG. 4 is a display diagram showing the manner in which thefacility preferably promotes an auction to identified users. FIG. 4shows that a promotion 410 is displayed on a display 400. The promotionmay preferably be sent to a user an email message, as an ICQ instantmessage, or as another type of message. The promotion may further bepresented to the user as a web page, either when the user visits a website for the operator of the facility, or when the user visits another,associated web site. The promotion may also be provided in audio form,such as in an automatically initiated telephone call or an automaticallydelivered voicemail message. Those skilled in the art will appreciatethat there are additional media through which the facility can alsoprovide promotions to selected users.

[0030] The promotion 410 invites the user to consider bidding on theselected auction. The promotion includes information 411 about theauction, such as a description of the item offered for sale, the time atwhich the auction opened, the time at which the auction will close, andthe current bid amount. Those skilled in the art will appreciate thatother information about the selected auction could also be included. Thepromotion preferably further includes a visual control 412, such as abutton, that the user may operate in order to open the web page used byusers to bid in the selected auction.

[0031] It will be understood by those skilled in the art that theabove-described facility could be adapted or extended in various ways.While the foregoing description makes reference to preferredembodiments, the scope of the invention is defined solely by the claimsthat follow and the elements recited therein.

I claim:
 1. A method in a computer system for identifying users to whomto promote a selected auction, the selected auction having users thathave already bid in the selected auction, the method comprising: foreach of a plurality of examined auctions other than the selectedauction, distinguishing the examined auction if the number of users thathave bid in the selected auction that also bid in the examined auctionexceeds a minimum threshold; and for each of the distinguished auctions,identifying users that bid in the distinguished auction and did not bidin the selected auction.
 2. The method of claim 1, further comprisingpromoting the selected auction to the identified users.
 3. The method ofclaim 1, further comprising transmitting electronic mail messages to theidentified users promoting the selected auction to them.
 4. The methodof claim 1, further comprising, when any of the identified users requesta selected web page: incorporating in the selected web page informationpromoting the selected auction; and after such incorporation, returningthe selected web page to the user.
 5. A computer-readable medium whosecontents cause a computer system to identify users to whom to promote aselected auction, the selected auction having users that have alreadybid in the selected auction, by: for an examined auction other than theselected auction, distinguishing the examined auction if the number ofusers that have bid in the selected auction that also bid in theexamined auction exceeds a minimum threshold; and if the examinedauction is distinguished, identifying users that bid in thedistinguished auction and did not bid in the selected auction.
 6. Thecomputer-readable medium of claim 5 wherein the contents of thecomputer-readable medium further cause the computer system to promotethe selected auction to the identified users.
 7. The computer-readablemedium of claim 5 wherein the contents of the computer-readable mediumfurther cause the computer system to transmit electronic mail messagesto the identified users promoting the selected auction to them.
 8. Thecomputer-readable medium of claim 5 wherein the contents of thecomputer-readable medium further cause the computer system to, when anyof the identified users request a selected web page: incorporate in theselected web page information promoting the selected auction; and aftersuch incorporation, return the selected web page to the user.
 9. Amethod in a computer system for identifying users to whom to promote aselected auction among a set of users, where a proper subset of theclustered users have already bid in the selected auction, the methodcomprising: applying clustering techniques to identify clusters of usersamong the set of users; and in response to a bid by a selected user inthe selected auction, identifying any cluster of which the selected useris a member, more than a threshold number of whose members have bid inthe selected auction, and identifying users of the identified clusterswho have not bid in the selected auction.
 10. The method of claim 9,further comprising determining to which of the users of the identifiedclusters who have not bid in the selected auction the selected auctionhas already been promoted, and wherein the identifying identifies usersof the identified clusters to whom the selected auction has not alreadybeen promoted.
 11. The method of claim 9, further comprising promotingthe selected auction to the identified users.
 12. The method of claim 9,further comprising transmitting electronic mail messages to theidentified users promoting the selected auction to them.
 13. The methodof claim 9, further comprising, when any of the identified users requesta selected web page: incorporating in the selected web page informationpromoting the selected auction; and after such incorporation, returningthe selected web page to the user.
 14. A computer-readable medium whosecontents cause a computer system to identify users to whom to promote aselected auction among a set of users, where a proper subset of theclustered users have already bid in the selected auction, by: applyingclustering techniques to identify a cluster of users among the set ofusers; and in response to a bid in the selected auction by a selecteduser that is a member of the cluster, identifying users of the clusterwho have not bid in the selected auction.
 15. The computer-readablemedium of claim 14 wherein the contents of the computer-readable mediumfurther cause the computer system to determine to which of the users ofthe identified clusters who have not bid in the selected auction theselected auction has already been promoted, and wherein the identifyingidentifies users of the identified clusters to whom the selected auctionhas not already been promoted.
 16. The computer-readable medium of claim14 wherein the contents of the computer-readable medium further causethe computer system to promote the selected auction to the identifiedusers.
 17. The computer-readable medium of claim 14 wherein the contentsof the computer-readable medium further cause the computer system totransmit electronic mail messages to the identified users promoting theselected auction to them.
 18. The computer-readable medium of claim 14wherein the contents of the computer-readable medium further cause thecomputer system to, when any of the identified users request a selectedweb page: incorporate in the selected web page information promoting theselected auction; and after such incorporation, return the selected webpage to the user.
 19. A method in a computer system for identifyingusers to whom to promote a selected auction, the selected auction havingusers that have already bid in the selected auction, the methodcomprising: maintaining a representation of bidding histories of aplurality of users that is a superset of the users that have already bidin the selected auction; and identifying users that have not already bidin the selected auction that have bidding histories that are similar tothose of users that have already bid in the selected auction.
 20. Themethod of claim 19 wherein users are identified using clusteringtechniques.
 21. The method of claim 19, further comprising promoting theselected auction to the identified users.
 22. The method of claim 19,further comprising transmitting electronic mail messages to theidentified users promoting the selected auction to them.
 23. The methodof claim 19, further comprising, when any of the identified usersrequest a selected web page: incorporating in the selected web pageinformation promoting the selected auction; and after suchincorporation, returning the selected web page to the user.
 24. Acomputer-readable medium whose contents cause a computer system toidentify users to whom to identify users to whom to promote a selectedauction, the selected auction having users that have already bid in theselected auction, by: maintaining a representation of bidding historiesof a plurality of users that is a superset of the users that havealready bid in the selected auction; and identifying users that have notalready bid in the selected auction that have bidding histories that aresimilar to those of users that have already bid in the selected auction.25. The computer-readable medium of claim 24 wherein users areidentified using clustering techniques.
 26. The computer-readable mediumof claim 24 wherein the contents of the computer-readable medium furthercause the computer system to promote the selected auction to theidentified users.
 27. The computer-readable medium of claim 24 whereinthe contents of the computer-readable medium further cause the computersystem to transmit electronic mail messages to the identified userspromoting the selected auction to them.
 28. The computer-readable mediumof claim 24 wherein the contents of the computer-readable medium furthercause the computer system to, when any of the identified users request aselected web page: incorporate in the selected web page informationpromoting the selected auction; and after such incorporation, return theselected web page to the user.
 29. A method in a computer system forpromoting a first auction in which a first user has bid, comprising:identifying a second user that has not bid in the first auction and thathas bid in a second auction in which the first user has bid; andpromoting the first auction to the second user.
 30. A computer systemfor promoting a first auction in which a first user has bid, comprising:a user identification subsystem adapted to identify a second user thathas not bid in the first auction and that has bid in a second auction inwhich the first user has bid; and an auction promotion subsystem adaptedto promote the first auction to the second user.